Universal Method for Determining Electrolyte Temperatures in Capillary Electrophoresis
Bibliographic record
Abstract
Temperature increase in capillary electrophoresis (CE) due to Joule heating is an inherent limitation of this powerful separation technique. Active cooling systems can decrease the temperature of a large part of the capillary but they leave "hot spots" at the capillary ends which can completely ruin some CE analyses despite their short lengths. Here, we introduce a "universal method for determining electrolyte temperatures" (UMET) that can determine temperatures in both efficiently- and inefficiently-cooled parts of the capillary. UMET can be applied to all electrolytes, as it does not involve any probe; it requires only measuring current versus voltage for different voltages and processing the data using an iterative algorithm. To demonstrate the universality of UMET, we measured temperatures for electrolytes of different ionic strengths as well as for different capillary diameters. We further propose a "simplified universal method for predicting electrolyte temperatures" (SUMET) which only requires one measurement of current and voltage (that can be completed in 1 min) and uses two empirical equations to predict temperatures in the efficiently- and inefficiently-cooled parts of the capillary. The equations include several instrument-specific empirical parameters that are determined using a large set of current-voltage data obtained with UMET for a range of electrolytes and different capillaries. To demonstrate the utility of SUMET, we obtained the required data set for a Beckman MDQ CE instrument and produced all required empirical parameters that enable a user of this instrument to predict the temperature for every new experimental set in a matter of minutes. We confirmed the accuracy of SUMET by measuring the temperature-sensitive dissociation rate constant of a protein-DNA complex. We foresee that UMET will be used to produce instrument-specific empirical parameters for all CE instruments and then SUMET will be routinely used for temperature prediction in CE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".